GCIRS-Reasoning-1.5B-R1

GCIRS-Reasoning-1.5B-R1 is a research-grade reasoning model fine-tuned from Qwen2.5-1.5B-Instruct, focused on non-fictional reasoning, factual consistency, and scientific depth. Trained with reinforcement learning using the Big Reasoning Traces dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments.

GGUF: https://huggingface.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF

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Key Features

1. Reinforcement Learning on Big Reasoning Traces Fine-tuned using DeepSeek’s Big Reasoning Traces, ensuring clarity in multi-step reasoning, factual deduction, and long-form scientific argumentation.

2. Research-Ready Scientific Fidelity Designed for researchers, educators, and analysts—offers reliable factual recall, logical structuring, and precise step-by-step explanation.

3. Structured Output in LaTeX, Markdown, and JSON Supports technical documentation and publishing with seamless integration of LaTeX equations, Markdown formatting, and JSON output.

4. Multilingual Technical Reasoning Effective across 20+ languages, especially in scientific, academic, and technical domains.

5. Efficient for Inference Despite its 1.5B parameter scale, it's optimized for low-latency inference across modern GPUs and research pipelines.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/GCIRS-Reasoning-1.5B-R1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the principle of entropy in thermodynamics with examples."

messages = [
    {"role": "system", "content": "You are a scientific reasoning assistant."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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Intended Use

• Scientific and research-grade question answering
• Conceptual explanations in physics, biology, and chemistry
• Factual, non-fictional structured content generation
• Academic tutoring and reasoning assessment
• High-fidelity inference in low-latency research settings

Limitations

• Not designed for casual chat or storytelling
• Performance may decline outside scientific/technical domains
• Limited creativity and abstract generalization
• Context limitations in extremely long research documents

References

1. Qwen2.5 Technical Report (2024)
2. Big Reasoning Traces (DeepSeek Research)
3. Reinforcement Learning with Human Feedback (RLHF)